Parametric POMDPs for planning in continuous state spaces
نویسندگان
چکیده
منابع مشابه
Parametric POMDPs for planning in continuous state spaces
Alex M. Brooks Doctor of Philosophy The University of Sydney January 2007 Parametric POMDPs for Planning in Continuous State Spaces This thesis is concerned with planning and acting under uncertainty in partially-observable continuous domains. In particular, it focusses on the problem of mobile robot navigation given a known map. The dominant paradigm for robot localisation is to use Bayesian e...
متن کاملContinuous State POMDPs for Object Manipulation Tasks
My research focus is on using continuous state partially observable Markov decision processes (POMDPs) to perform object manipulation tasks using a robotic arm. During object manipulation, object dynamics can be extremely complex, non-linear and challenging to specify. To avoid modeling the full complexity of possible dynamics, I instead use a model which switches between a discrete number of s...
متن کاملPOMCPOW: An online algorithm for POMDPs with continuous state, action, and observation spaces
Online solvers for partially observable Markov decision processes have been applied to problems with large discrete state spaces, but continuous state, action, and observation spaces remain a challenge. This paper begins by investigating double progressive widening (DPW) as a solution to this challenge. However, we prove that this modification alone is not sufficient because the belief represen...
متن کاملContinuous-State POMDPs with Hybrid Dynamics
Continuous-state POMDPs provide a natural representation for a variety of tasks, including many in robotics. However, existing continuous-state POMDP approaches are limited by their reliance on a single linear model to represent the world dynamics. We introduce a new switching-state (hybrid) dynamics model that can represent multi-modal state-dependent dynamics. We present a new point-based POM...
متن کاملEfficient Planning and Tracking in POMDPs with Large Observation Spaces
Planning in partially observable MDPs is computationally limited by the size of the state, action and observation spaces. While many techniques have been proposed to deal with large state and action spaces, the question of automatically finding good low-dimensional observation spaces has not been explored as thoroughly. We show that two different reduction algorithms, one based on clustering an...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Robotics and Autonomous Systems
سال: 2006
ISSN: 0921-8890
DOI: 10.1016/j.robot.2006.05.007